321–330 of 1001 posts
Kimi K3 blog is up: https://www.kimi.com/blog/kimi-k3 2.8T param open model, 1M context, native vision. Weights releasing by July 27 with technical report. Launching with max thinking effort by default; low/high effort modes coming in future updates.
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This is weird and reactionary. Lots of organizations are continuing to refuse to use chinese models due to security and IP concerns. Anthropic/american models aren't going anywhere anytime soon.
> Lots of organizations are continuing to refuse to use chinese models Correction: Lots of organizations are refusing to use Anthropic Fable because they have forced opt-in data collection as part of their privacy policy, even for Enterprise.
Not everyone's going to care about Anthropic requiring data collection (a similar debate plays out with regards to "pay or consent" on website tracking), just as not everyone cares about China with regards to security/IP issues (if they did, a lot more would be banned besides occasionally-Huawei).
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Free use without registration -> free to anyone and anything -> easy to abuse at scale, with no way to restrict use.
You can limit it a lot to minimize the abuse. In free entrypoint, set token and context limits to be very small. Limit to 2 prompts per IP or something every X hour. That is already a substantial limit where bypassing might not provide much benefits.
Another deepseek moment? it seems they have fully caught with fable tier of models, and this was a lot sooner than was expected.
Yeah, I would have expected Zhipu to ship a Fable-adjacent model by the end of the year, but the jump from Kimi 2.7 (which I think is just barely at the level where it is genuinely helpful for coding) to this is absolutely bonkers. And this is clearly not just benchmaxing; this thing actually works. If you told me I could only use this and never use Fable or Sol again, I'd shrug and not feel like I'd lost much.
There were talks of a GLM 5.3 in August, so maybe not that far away...
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This is weird and reactionary. Lots of organizations are continuing to refuse to use chinese models due to security and IP concerns. Anthropic/american models aren't going anywhere anytime soon.
> Lots of organizations are continuing to refuse to use chinese models due to security and IP concerns This is such a common omission: the Chinese models are open, you can host them yourself on your premises. So privacy and independence.
while I am skeptical that this is happening atm, there are probably many industries where the risk does not seem worthwhile
Kimi doesn't do well on my "ask a trivia question that other AIs get wrong" test. The question it came up with, "which U.S. state is closest to Africa?" is a pretty standard trivia question without any reason to believe other AIs would get confused. https://pellmell.ai/s/dccdeca69f929f79bc89317035610049 Even GPT-OSS-120b gets this right: https://pellmell.ai/s/1a43dfc7a3baa214aa0fa1b95d2c536a
Working with chinese models is giving me a fullfilment sensation. I think that I have enough quality for the work that I need to do and lots of extra tokens to work with. With Claude and ChatGPT I reach the limits fairly easy, but not with OpenCode Go. So I will use Claude once in a while for difficult tasks to see how much better it still is (but use Chinese on a daily basis)
Anthropic's "durable advantage" theory of US AI dominance is looking pretty silly. There's zero indication that it will be hard for China to keep pace as models improve and start contributing to their own training. Which pretty much invalidates their policy recommendations. They can't even blame it on distillation this time, unless they want to claim that their own preferred security measures were ineffective in prev…
Likely won't improve much. They trained on every text already.
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> Maybe another DeepSeek moment right here. Surely not... What made DeepSeek disruptive was that the cost was 10X lower. In this case, the cost is about 2X lower the Sol I think? At 2X, you're pretty close to the error margins due to token efficiency etc... I'd say this is "on trend" for open models catching up to frontier labs, but its not a "change in the trend" like DeepSeek was IMO.
DeepSeek didn’t really change any trends though, unless you count the stock market. It was impressive work, but models were commoditizing and inference costs were dropping rapidly already. They were neither the first nor the last 10x optimization, from what I’ve seen.
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